TY - JOUR
T1 - Development of Artificial Intelligence Models for Tooth Numbering and Detection
T2 - A Systematic Review
AU - Maganur, Prabhadevi C.
AU - Vishwanathaiah, Satish
AU - Mashyakhy, Mohammed
AU - Abumelha, Abdulaziz S.
AU - Robaian, Ali
AU - Almohareb, Thamer
AU - Almutairi, Basil
AU - Alzahrani, Khaled M.
AU - Binalrimal, Sultan
AU - Marwah, Nikhil
AU - Khanagar, Sanjeev B.
AU - Manoharan, Varsha
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/10
Y1 - 2024/10
N2 - Dental radiography is widely used in dental practices and offers a valuable resource for the development of AI technology. Consequently, many researchers have been drawn to explore its application in different areas. The current systematic review was undertaken to critically appraise developments and performance of artificial intelligence (AI) models designed for tooth numbering and detection using dento-maxillofacial radiographic images. In order to maintain the integrity of their methodology, the authors of this systematic review followed the diagnostic test accuracy criteria outlined in PRISMA-DTA. Electronic search was done by navigating through various databases such as PubMed, Scopus, Embase, Cochrane, Web of Science, Google Scholar, and the Saudi Digital Library for the articles published from 2018 to 2023. Sixteen articles that met the inclusion exclusion criteria were subjected to risk of bias assessment using QUADAS-2 and certainty of evidence was assessed using GRADE approach.AI technology has been mainly applied for automated tooth detection and numbering, to detect teeth in CBCT images, to identify dental treatment patterns and approaches. The AI models utilised in the studies included exhibited a highest precision of 99.4% for tooth detection and 98% for tooth numbering. The use of AI as a supplementary diagnostic tool in the field of dental radiology holds great potential.
AB - Dental radiography is widely used in dental practices and offers a valuable resource for the development of AI technology. Consequently, many researchers have been drawn to explore its application in different areas. The current systematic review was undertaken to critically appraise developments and performance of artificial intelligence (AI) models designed for tooth numbering and detection using dento-maxillofacial radiographic images. In order to maintain the integrity of their methodology, the authors of this systematic review followed the diagnostic test accuracy criteria outlined in PRISMA-DTA. Electronic search was done by navigating through various databases such as PubMed, Scopus, Embase, Cochrane, Web of Science, Google Scholar, and the Saudi Digital Library for the articles published from 2018 to 2023. Sixteen articles that met the inclusion exclusion criteria were subjected to risk of bias assessment using QUADAS-2 and certainty of evidence was assessed using GRADE approach.AI technology has been mainly applied for automated tooth detection and numbering, to detect teeth in CBCT images, to identify dental treatment patterns and approaches. The AI models utilised in the studies included exhibited a highest precision of 99.4% for tooth detection and 98% for tooth numbering. The use of AI as a supplementary diagnostic tool in the field of dental radiology holds great potential.
KW - Artificial intelligence
KW - CNN
KW - Radiographic images
KW - Tooth detection
KW - Tooth numbering
UR - http://www.scopus.com/inward/record.url?scp=85195520562&partnerID=8YFLogxK
U2 - 10.1016/j.identj.2024.04.021
DO - 10.1016/j.identj.2024.04.021
M3 - Review article
C2 - 38851931
AN - SCOPUS:85195520562
SN - 0020-6539
VL - 74
SP - 917
EP - 929
JO - International Dental Journal
JF - International Dental Journal
IS - 5
ER -